1 INTRODUCTION

1.1 Average Length of Stay (ALOS)

The average length of stay is a broad term that that is used to evaluate hospital efficiency and patient management. It mainly consists of two kinds:

  • Impatient Care Average Length of Stay

  • Curative Care Average Length of Stay

Data regarding the above topic is found within the Healthcare Utilization: Hospital aggregate dataset. Click Here to view the dataset.

This report focuses more on the Average length of stay in the organisation for economic co-operation & development countries.

The code chunk written below will contains libraries that will be loaded from respective packages which will be used on the course of this Analytic report.

pacman:: p_load(
  "rmarkdown", "tinytex", "tidyverse", "knitr", 
  "prettydoc", "rmdformats", "kableExtra", 
  "plotly", "visdat", "here", "finalfit", 
  "janitor", "patchwork")

1.2 Load Raw Dataset

This is the Hospital Aggregates raw data set which includes the Impatient care Average length of stay and Curative care average length of stay that will be extracted for insights

# Importing and loading Hospital Aggregates Dataset in a directory
Hospital_Aggregates_raw <- read_csv(here("Data/HU_HA.csv"))
Hospital_Aggregates_raw
## # A tibble: 3,845 × 11
##    VAR      Variable  UNIT  Measure COU   Country   YEA  Year Value `Flag Codes`
##    <chr>    <chr>     <chr> <chr>   <chr> <chr>   <dbl> <dbl> <dbl> <chr>       
##  1 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2010  2010   0.8 <NA>        
##  2 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2011  2011   0.8 <NA>        
##  3 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2012  2012   0.8 B           
##  4 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2013  2013   0.8 <NA>        
##  5 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2014  2014   0.8 <NA>        
##  6 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2015  2015   0.8 B           
##  7 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2016  2016   0.8 <NA>        
##  8 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2017  2017   0.8 <NA>        
##  9 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2018  2018   0.8 <NA>        
## 10 HUTIJOCS Curative… NBPH… Number… AUS   Austra…  2019  2019   0.8 <NA>        
## # ℹ 3,835 more rows
## # ℹ 1 more variable: Flags <chr>

1.3 Raw Data Structure and Summary

Now let’s look at the structure and summmary of our dataset and prior to data cleaning

# familiarizing and understanding the dataset
head(Hospital_Aggregates_raw)
## # A tibble: 6 × 11
##   VAR      Variable   UNIT  Measure COU   Country   YEA  Year Value `Flag Codes`
##   <chr>    <chr>      <chr> <chr>   <chr> <chr>   <dbl> <dbl> <dbl> <chr>       
## 1 HUTIJOCS Curative … NBPH… Number… AUS   Austra…  2010  2010   0.8 <NA>        
## 2 HUTIJOCS Curative … NBPH… Number… AUS   Austra…  2011  2011   0.8 <NA>        
## 3 HUTIJOCS Curative … NBPH… Number… AUS   Austra…  2012  2012   0.8 B           
## 4 HUTIJOCS Curative … NBPH… Number… AUS   Austra…  2013  2013   0.8 <NA>        
## 5 HUTIJOCS Curative … NBPH… Number… AUS   Austra…  2014  2014   0.8 <NA>        
## 6 HUTIJOCS Curative … NBPH… Number… AUS   Austra…  2015  2015   0.8 B           
## # ℹ 1 more variable: Flags <chr>
# Visualizing the variable types
vis_dat(Hospital_Aggregates_raw)

distinct(Hospital_Aggregates_raw , Variable) # No. of Variables present
## # A tibble: 6 × 1
##   Variable                                             
##   <chr>                                                
## 1 Curative care bed-days                               
## 2 Curative care occupancy rate                         
## 3 Curative care discharges                             
## 4 Inpatient care discharges (all hospitals)            
## 5 Inpatient care average length of stay (all hospitals)
## 6 Curative care average length of stay

1.4 Data Cleaning 1.0

The Hospital Aggregate raw data set is explored and wrangled to get complete and sufficient data as the average days for both Inpatient care ALOS and Creative care ALOS is determined in the last decade.

data_hg <- Hospital_Aggregates_raw %>% 
  select(Year, Country, Variable, Measure, Value) %>%
  filter(Variable %in% c("Inpatient care average length of stay (all hospitals)", 
                         "Curative care average length of stay")) %>% 
  clean_names() %>% 
  filter(!(year %in% c(2021:2022)) & !(country %in% c("Brazil",
                                                      "Bulgaria",
                                                      "China (People's Republic of)", 
                                                      "Croatia",
                                                      "Romania",
                                                      "Russia",
                                                      "South Africa"))) %>% # exclude all non-oecd countries
  drop_na() %>% #drop all rows with NA values 
  arrange(year)

data_hg %>% 
  distinct(measure) # check for distinct observation
## # A tibble: 1 × 1
##   measure
##   <chr>  
## 1 Days
data_hg %>% 
  tabyl(country) %>% # Tabulate the data by country and counts no. of values 
  filter(n < 22) # 22 values for each year and remove countries less than 22
##          country  n     percent
##           Canada 11 0.014627660
##            Chile 11 0.014627660
##         Colombia  8 0.010638298
##          Denmark 11 0.014627660
##           Greece 12 0.015957447
##           Mexico 11 0.014627660
##      Netherlands  7 0.009308511
##  Slovak Republic 21 0.027925532
mean_year <- data_hg %>% 
  filter(!(country %in% c("Canada",
                   "Chile",
                   "Colombia", 
                   "Denmark",
                   "Greece",
                   "Mexico",
                   "Netherlands",
                   "Slovak Republic"))) %>%
  group_by(year, variable, measure) %>% 
  summarize(mean_value = mean(value)) %>% 
  mutate(mean_value = round(mean_value, 1)) %>% 
  ungroup()

1.5 Vizualizing Average Length of Stay

A line graph showing the difference between the Curative care ALOS and Inpatient care ALOS from 2010-2020.

gap_years <- seq(from = 2010, to = 2020, by = 1) # gap of 1 year from 2010-2020

  ggplot(mean_year, mapping = aes(x = year, y = mean_value, color = variable)) +
  geom_line(linewidth = 1) + geom_point(color = "black") + 
  scale_x_continuous(breaks = gap_years) + 
  geom_text(aes(label = mean_value, vjust = -0.3)) + theme(legend.position = "top") +
  theme(legend.title = element_blank()) +
  labs(title = "AVERAGE LENGTH OF STAY",
       subtitle = "Curative care ALOS vs Impatient care ALOS in the Last Decade",
       x = "Year", y = "Average Days",
       caption = "source: https://stats.oecd.org
       accessed Sunday 7th November, 2023")

From the Plot above, one can tell that Impatient ALOS tends to be higher than the Curative ALOS. Going further, the two variables down trended as years went by in the last decade with a marked uptrend in 2020. This is sign that most OECD countries are doing fine in terms of Healthcare efficiency

Overall, It shows that curative care could be a subset to inpatient care since curative care may lead to inpatient care if triggered by some factors and not the other way round.

1.6 Pivoting from long to wide data

The dataset is currently a long data but we need to change to wide data in other to access and easily wrangle the Variable and Measure columns with there respective Values. At the end, the column names are cleaned to snake case for better manipulation.

# Pivoting from long to wide data
wide_data <- Hospital_Aggregates_raw %>%
  pivot_wider(names_from = c(Variable, Measure),
              values_from = Value,
              id_cols = c(Year, Country))%>% 
  clean_names() # clean names to lower and snake case for easy manipulation

1.7 Data Cleaning 2.0

Now Data cleaning is done by selecting and renaming the main variables for the report:

  • Impatient Care Average Length of Stay

  • Curative Care Average Length of Stay

The year and country variables are needed too during the analysis.

Going forward, since the report is on about the last decade, the year 2021 & 2022 is filtered out of the data and the Non-organisation for economic co-operation & development countries (Non-OECD), thereby isolating only the organisation for economic co-operation & development countries (OECD) for the report.

All rows containing NA values are dropped as only countries with complete data on the Inpatient ALOS Care and Curative Care ALOS are needed for further analysis

clean_data <- wide_data %>%
  select(year, country, 
         inpatient_care_alos = inpatient_care_average_length_of_stay_all_hospitals_days, 
         curative_care_alos = curative_care_average_length_of_stay_days) %>%
  filter(!(year %in% c(2021:2022)) & !(country %in% c("Brazil",
                        "Bulgaria",
                        "China (People's Republic of)", 
                        "Croatia",
                        "Romania",
                        "Russia",
                        "South Africa"))) %>% # exclude year: 2021-2022 a& all non-oecd countries
  drop_na() %>% # Drop all rows with NA values
  arrange(country) # Arrange by ascending order by default

It appears Greece and Slovakia Republic had empty cells for 5 years and a year respectively. So, we filter them out as other countries with NA values has already been dropped too. All these filtering and dropping helps to give a good sample of complete data to work with.

# Check and view for missing value either in Impatient ALOS or Curative ALOS
clean_data %>% 
  tabyl(country, year) %>% # tabulates country on rows and year columns 
  View()

oecd_countries <- clean_data %>% 
  filter(!(country %in% c("Greece", "Slovak Republic"))) 
# Both countries had empty values in 1 or more years

# Cross check if code was successful
oecd_countries %>%
  tabyl(country, year) %>% 
  View()

2 INPATIENT CARE ALOS

The Inpatient care average length of stay (ALOS) is a crucial metric that measures the duration of a patient’s hospitalization. It is often used as an indicator of efficiency. Understanding the determinants of ALOS can shed light on healthcare resource utilization, patient outcomes, and the efficiency of healthcare delivery. This is done in a hospital.

Formula:

Total number of days stayed by all inpatients during a year divided by the number of admissions or discharges. The indicator excludes days cases.

3 CURATIVE CARE ALOS

Curative care, also known as acute care, is the type of medical care focused on diagnosing and treating acute conditions or injuries. It involves hospitalization and is generally aimed at providing immediate relief and restoring health. This can be done in hospital, clinics, homes etc. This technically means that the curative care ALOS is a subset of Inpatient Care ALOS.

Formula:

Number of curative care bed-days divided divided by number of curative care discharge during the year

4 FACTORS AFFECTING ALOS & IT’S IMPLICATIONS

4.1 Factors:

  1. Severity and Complexity of Illness: The severity and complexity of a patient’s condition significantly impact the ALOS. Patients with more severe or complex illnesses such as cancer or cardiovascular disorders, tended to have longer hospital stays for diagnosis, treatment, and monitoring (Verweij et al., 2017).

  2. Comorbidities: The presence of comorbidities, which refers to the simultaneous occurrence of multiple medical conditions, can complicate treatment plans and extend the hospital stay. Patients with comorbidities often require additional medical tests, consultations with various specialists, and tailored treatment approaches (Rothberg et al., 2017).

  3. Invasive Procedures: The need for invasive procedures, such as surgeries or interventional treatments, is another factor influencing ALOS. These procedures often require pre-operative preparation, the actual operation, and post-operative care and monitoring. The complexity and duration of the procedure, as well as any complications that may arise, can extend the hospital stay (Chakkera et al., 2018).

  4. Diagnostic Evaluations: The length of hospital stay can be influenced by the time required to conduct diagnostic evaluations. Diagnostic procedures, including laboratory tests, radiological investigations, and specialized consultations, contribute to the ALOS. Delays in obtaining test results or consultation appointments can prolong hospitalization (Gandelman et al., 2019).

  5. Bed Availability and Resource Constraints: Hospital resource availability, such as the number of available beds or healthcare personnel, plays a role in determining ALOS. When hospitals experience bed shortages or resource constraints, patients may experience longer waits for admission or discharge. Additionally, limited resources can impact patient flow, leading to extended stays (Hamblin et al., 2016). —

4.2 Implications:

  1. Healthcare Resource Utilization: ALOS is an essential indicator of healthcare resource utilization. Longer hospital stays require greater allocation of resources, including bed occupancy, staffing, medications, and equipment. Understanding ALOS can assist healthcare systems in optimizing resource planning and capacity management.

  2. Cost Implications: Longer hospital stays directly impact healthcare costs. Prolonged hospitalization increases both direct medical costs, such as medications and procedures, as well as indirect costs, including longer-term care, rehabilitation, and associated services. Reducing ALOS can optimize healthcare spending and allow for better resource allocation.

  3. Patient Experience and Outcomes: A shorter ALOS can positively impact patient experiences and outcomes. Longer hospital stays may lead to increased risk of hospital-acquired infections, reduced mobility, psychological distress, and decreased overall satisfaction with care. Minimizing ALOS can improve patient comfort and satisfaction, potentially leading to better health outcomes.

  4. Capacity Management and Access: Understanding ALOS helps healthcare facilities manage their capacity efficiently. By monitoring ALOS trends, hospitals can identify potential bottlenecks, optimize patient flow, and reduce waiting times for admissions and procedures. This can improve access to care, enhance operational efficiency, and reduce overcrowding.

5 INPATIENT CARE ALOS vs CURATIVE CARE ALOS

From the data frame of the oecd_countries, the mean ALOS for respective countries can be calculated by grouping by country and summarizing the mean for both Impatient Care ALOS and Curative Care ALOS

mean_oecd_countries <- oecd_countries %>% 
  group_by(country) %>% 
  summarise(mean_inpatient_care_alos = mean(inpatient_care_alos), 
            mean_curative_care_alos = mean(curative_care_alos)) %>% 
  mutate(mean_inpatient_care_alos = round(mean_inpatient_care_alos, 1), 
         mean_curative_care_alos = round(mean_curative_care_alos, 1)) %>% 
  arrange(country)

5.1 Data Table

Now a glimpse of a structured table for the cleaned data set is produced. A randomly selected rows of the data table mean_oecd_countries can be seen and understood according to respective variable.

mean_oecd_countries %>% 
  slice(1:3, 15:17, 27:30) %>% 
  kable(col.names = c("Country",
                      "Inpatient Care ALOS",
                      "Curative Care ALOS")) %>% 
  kable_styling(bootstrap_options = "striped", full_width = F)
Country Inpatient Care ALOS Curative Care ALOS
Australia 5.4 4.7
Austria 8.2 6.4
Belgium 7.5 6.8
Japan 29.6 16.8
Korea 17.4 8.4
Latvia 8.3 5.9
Switzerland 8.5 7.2
Türkiye 4.1 4.0
United Kingdom 7.1 6.1
United States 6.1 5.5

5.2 Inpatient Care ALOS

Plotting the Mean Inpatient care ALOS for respective OECD Countries in the past decade

ggplot(mean_oecd_countries, mapping = aes(x = reorder(country, mean_inpatient_care_alos), 
                                          y = mean_inpatient_care_alos, 
                                    group = country, fill = country)) + 
  geom_col() + coord_flip() + theme_bw() +
  geom_text(aes(label = mean_inpatient_care_alos, vjust = 0.4, hjust = -0.1)) +
  theme(legend.position = "none") +
  labs(title = "Inpatient Care Average Length of Stay within the last decade", 
       subtitle = "Inpatient Care ALOS across the OECD Countries in Days", 
       x = "OECD Countries", y = "Inpatient Care Average Lenght of Stay",
       caption = "source: https://stats.oecd.org
       Accessed Sunday 17th November, 2023")

Although both curative care ALOS and inpatient care ALOS are dropping over the years, countries such as Japan and Korea had the longest Inpatient average length of stay with 29.6 and 17.4 days respectively. Majority of the countries fell between 5.4 and 9.6 days, with Turkiye standing out with the best Days of 4.1

This means Japan and Korea has a poor healthcare efficiency and patient management and would be needing more investment in to healthcare facilities and resources from the health government to mitigate this high days.

5.3 Curative Care ALOS

Plotting the Mean Curative care ALOS for respective OECD Countries in the past decade

ggplot(mean_oecd_countries, mapping = aes(x = reorder(country, mean_curative_care_alos), 
                                            y = mean_curative_care_alos, 
                                            fill = country)) + 
  geom_col() + coord_flip() + theme_bw() +
  geom_text(aes(label = mean_curative_care_alos, vjust = 0.4, hjust = -0.1)) +
  theme(legend.position = "none") +
  labs(title = "Curative Care Average Length of Stay within the last decade", 
       subtitle = "Curative Care ALOS across the OECD Countries in Days", 
       x = "OECD Countries", y = "Curative Care Average Lenght of Stay",
       caption = "source: https://stats.oecd.org
       Accessed Sunday 17th November, 2023")

Japan still had the longest Curative care average length of stay with 16.8 days

This is a confirmation that Japan has a poor healthcare efficiency and patient management and would be needing more investment in to healthcare facilities and resources from the health government.

It appears Turkiye is still the best overall with just 4 days, followed by Australia and Israel. This signifies a very productive, organised and efficient healthcare system

mi <- ggplot(mean_oecd_countries, mapping = aes(x = reorder(country, mean_inpatient_care_alos), 
                                          y = mean_inpatient_care_alos, 
                                    group = country, fill = country)) + 
  geom_col() + coord_flip() + theme_bw() +
  geom_text(aes(label = mean_inpatient_care_alos, vjust = 0.4, hjust = -0.1)) +
  theme(legend.position = "none") +
  labs(title = "Inpatient Care Average Length of Stay within the last decade", 
       subtitle = "Inpatient Care ALOS across the OECD Countries in Days", 
       x = "OECD Countries", y = "Inpatient Care Average Lenght of Stay",
       caption = "source: https://stats.oecd.org
       Accessed Sunday 7th November, 2023")

mc <- ggplot(mean_oecd_countries, mapping = aes(x = reorder(country, mean_curative_care_alos), 
                                            y = mean_curative_care_alos, 
                                            fill = country)) + 
  geom_col() + coord_flip() + theme_bw() +
  geom_text(aes(label = mean_curative_care_alos, vjust = 0.4, hjust = -0.1)) +
  theme(legend.position = "none") +
  labs(title = "Curative Care Average Length of Stay within the last decade", 
       subtitle = "Curative Care ALOS across the OECD Countries in Days", 
       x = "OECD Countries", y = "Curative Care Average Lenght of Stay",
       caption = "source: https://stats.oecd.org
       Accessed Sunday 17th November, 2023")

mi + mc +
  plot_annotation(title = "AVERAGE LENGTH OF STAY",
                  subtitle = "Mean Average Length of Stay in OECD Nations", 
                  tag_levels = "I") + 
  plot_layout(guides = "collect")

7 CONCLUSION

It was observed that as Curative care days dropped, Inpatient care days dropped as years went on across most countries. This indicates a positive correlation between the two variables.

This could mean that aiming for shorter Curative care days could further shorten the Inpatient care days since the former can be a subset of the later thereby improving healthcare efficiency in a given country and across the globe.

Some of the times, Patients who require curative care could end up receiving impatient care due to some of the factors that can increase the length of stay. This is are most time beyond the hospital power and could have negative impact on the healthcare system.

8 RECOMMENDATION

Recent trends signify a shift towards shorter hospital stays due to advancements in medical technology, increased emphasis on ambulatory care, improved discharge planning, and the integration of home healthcare services (Curative Care).

As healthcare continues to evolve, improving efficiency and reducing the ALOS should remain a priority to enhance patient outcomes and optimize resource utilization.

Healthcare Researchers can understand ALOS in hospitals and know that the inpatient care ALOS is the major indicator for efficiency.

Although Having fully equipped hospitals settings in a given country is paramount to measuring the Inpatient Care ALOS, investment into curative care by integration of home care services and more ambulatory care is a big advantage as this can positively impact Inpatient ALOS days by reducing the amount of beds occupied my patients, thereby facilitating better patient management for the few admitted patient. This topic appears to be statistically proven.

9 REFERNCES

  1. Chakkera, H. A., et al. (2018). Surgical Procedures and Length of Stay for Kidney Transplantation: A Single-Center Experience. Transplantation Proceedings, 50(6), 1713-1716.

  2. Gandelman, G., et al. (2019). A novel laboratory-based admissions risk score to predict the average length of stay. PloS One, 14(6), e0218671.

  3. Hamblin, P., et al. (2016). Hospital utilization and average length of stay in the emergency department: a benchmarking approach. BMC Health Services Research, 16(1), 1-7.

  4. Rothberg, M. B., et al. (2017). Factors associated with the costs and outcomes of hospitalized patients with exacerbation of chronic obstructive pulmonary disease. Journal of General Internal Medicine, 32(2), 159-165.

  5. Verweij, L., et al. (2017). Patient characteristics and treatment patterns contributing to long hospital stays in patients with major depressive disorder. PloS One, 12(3), e0170748.

by B239464 to Health Researchers